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Trajectory outlier detection: New problems and solutions for smart cities (2021)
Journal Article
Lin, J. C. W., Djenouri, D., Djenouri, Y., & LIN, J. C. (2021). Trajectory outlier detection: New problems and solutions for smart cities. ACM Transactions on Knowledge Discovery from Data, 15(2), https://doi.org/10.1145/3425867

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for th... Read More about Trajectory outlier detection: New problems and solutions for smart cities.

When the decomposition meets the constraint satisfaction problem (2020)
Journal Article
Djenouri, Y., Djenouri, D., Habbas, Z., Lin, J. C., Michalak, T. P., & Cano, A. (2020). When the decomposition meets the constraint satisfaction problem. IEEE Access, 8, 207034-207043. https://doi.org/10.1109/access.2020.3038228

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is th... Read More about When the decomposition meets the constraint satisfaction problem.

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection (2020)
Journal Article
Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Lin, J. C., & Fortino, G. (2021). Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection. Information Fusion, 65, 13-20. https://doi.org/10.1016/j.inffus.2020.08.003

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories... Read More about Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection.

A recurrent neural network for urban long-term traffic flow forecasting (2020)
Journal Article
Belhadi, A., Djenouri, Y., Djenouri, D., & Lin, J. C. (2020). A recurrent neural network for urban long-term traffic flow forecasting. Applied Intelligence, 50(10), 3252-3265. https://doi.org/10.1007/s10489-020-01716-1

This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, na... Read More about A recurrent neural network for urban long-term traffic flow forecasting.

DFIOT: Data Fusion for Internet of Things (2020)
Journal Article
Boulkaboul, S., & Djenouri, D. (2020). DFIOT: Data Fusion for Internet of Things. Journal of Network and Systems Management, 28(4), 1136-1160. https://doi.org/10.1007/s10922-020-09519-y

In Internet of Things (IoT) ubiquitous environments, a high volume of heterogeneous data is produced from different devices in a quick span of time. In all IoT applications, the quality of information plays an important role in decision making. Data... Read More about DFIOT: Data Fusion for Internet of Things.

IoT-DMCP: An IoT data management and control platform for smart cities (2019)
Conference Proceeding
Boulkaboul, S., Djenouri, D., Bouhafs, S., & Belaid, M. (2019). IoT-DMCP: An IoT data management and control platform for smart cities. https://doi.org/10.5220/0007861005780583

This paper presents a design and implementation of a data management platform to monitor and control smart objects in the Internet of Things (IoT). This is through IPv4/IPv6, and by combining IoT specific features and protocols such as CoAP, HTTP and... Read More about IoT-DMCP: An IoT data management and control platform for smart cities.

A novel parallel framework for metaheuristic-based frequent itemset mining (2019)
Conference Proceeding
Fournier-Viger, P., Bendjoudi, A., Belhadi, A., Djenouri, D., Djenouri, Y., & Lin, J. C. (2019). A novel parallel framework for metaheuristic-based frequent itemset mining. https://doi.org/10.1109/cec.2019.8790116

Frequent Itemset Mining (FIM) is an important but very time-consuming data mining task. As a result, traditional FIM algorithms are often not scalable to large databases. To address this issue, several metaheuristics have been developed in recent yea... Read More about A novel parallel framework for metaheuristic-based frequent itemset mining.

Wireless energy efficient occupancy-monitoring system for smart buildings (2019)
Journal Article
Lasla, N., Doudou, M., Djenouri, D., Ouadjaout, A., & Zizoua, C. (2019). Wireless energy efficient occupancy-monitoring system for smart buildings. Pervasive and Mobile Computing, 59, https://doi.org/10.1016/j.pmcj.2019.101037

© 2019 Elsevier B.V. Rationalizing energy consumption in smart buildings is considered in this paper, and a wireless monitoring system based on Passive Infrared sensors (PIRs) is proposed. The proposed system is pervasive and can be integrated in exi... Read More about Wireless energy efficient occupancy-monitoring system for smart buildings.

Machine learning for smart building applications: Review and taxonomy (2019)
Journal Article
Djenouri, D., Laidi, R., Djenouri, Y., & Balasingham, I. (2019). Machine learning for smart building applications: Review and taxonomy. ACM Computing Surveys, 52(2), 1-36. https://doi.org/10.1145/3311950

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first clas... Read More about Machine learning for smart building applications: Review and taxonomy.

A Survey on Urban Traffic Anomalies Detection Algorithms (2019)
Journal Article
Djenouri, Y., Belhadi, A., Lin, J. C., Djenouri, D., & Cano, A. (2019). A Survey on Urban Traffic Anomalies Detection Algorithms. IEEE Access, 7, 12192-12205. https://doi.org/10.1109/access.2019.2893124

© 2019 IEEE. This paper reviews the use of outlier detection approaches in urban traffic analysis. We divide existing solutions into two main categories: flow outlier detection and trajectory outlier detection. The first category groups solutions tha... Read More about A Survey on Urban Traffic Anomalies Detection Algorithms.

Frequent itemset mining in big data with effective single scan algorithms (2018)
Journal Article
Djenouri, Y., Djenouri, D., Chun-Wei Lin, J., & Belhadi, A. (2018). Frequent itemset mining in big data with effective single scan algorithms. IEEE Access, 6, 68013-68026. https://doi.org/10.1109/ACCESS.2018.2880275

© 2013 IEEE. This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel imp... Read More about Frequent itemset mining in big data with effective single scan algorithms.

Adaptive learning-enforced broadcast policy for solar energy harvesting wireless sensor networks (2018)
Journal Article
Khiati, M., & Djenouri, D. (2018). Adaptive learning-enforced broadcast policy for solar energy harvesting wireless sensor networks. Computer Networks, 143, 263-274. https://doi.org/10.1016/j.comnet.2018.07.016

© 2018 Elsevier B.V. The problem of message broadcast from the base station (BS) to sensor nodes (SNs) in solar energy harvesting enabled wireless sensor networks is considered in this paper. The aim is to ensure fast and reliable broadcast without d... Read More about Adaptive learning-enforced broadcast policy for solar energy harvesting wireless sensor networks.

Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases (2018)
Journal Article
Djenouri, Y., Djenouri, D., Belhadi, A., Fournier-Viger, P., Chun-Wei Lin, J., & Bendjoudi, A. (2019). Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases. Information Sciences, 496, 326-342. https://doi.org/10.1016/j.ins.2018.06.060

© 2018 This paper investigates the use of GPU (Graphics Processing Unit) in improving the bees swarm optimization metaheuristic performance for solving the association rule mining problem. Although this metaheuristic proved its effectiveness, it requ... Read More about Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases.

Bee swarm optimization for solving the MAXSAT problem using prior knowledge (2017)
Journal Article
Djenouri, D., Habbas, Z., Djenouri, Y., & Fournier-Viger, P. (2019). Bee swarm optimization for solving the MAXSAT problem using prior knowledge. Soft Computing, 23(9), 3095-3112. https://doi.org/10.1007/s00500-017-2956-1

This paper explores rule decomposition for solving the MAXSAT problem. Four approaches are proposed to steer a bee swarm optimization metaheuristic. Two decomposition methods are proposed: direct and indirect. The first one applies the Kmeans algorit... Read More about Bee swarm optimization for solving the MAXSAT problem using prior knowledge.

Data Mining-Based Decomposition for Solving the MAXSAT Problem: Toward a New Approach (2017)
Journal Article
Djenouri, Y., Habbas, Z., & Djenouri, D. (2017). Data Mining-Based Decomposition for Solving the MAXSAT Problem: Toward a New Approach. IEEE Intelligent Systems, 32(4), 48-58. https://doi.org/10.1109/MIS.2017.3121546

This article explores advances in the data mining arena to solve the fundamental MAXSAT problem. In the proposed approach, the MAXSAT instance is first decomposed and clustered by using data mining decomposition techniques, then every cluster resulti... Read More about Data Mining-Based Decomposition for Solving the MAXSAT Problem: Toward a New Approach.

Temporal and spatial coherence verification in SMIL documents with hoare logic and disjunctive constraints: A hybrid formal method (2017)
Journal Article
Mekahlia, F. Z., Ghomari, A., Yazid, S., & Djenouri, D. (2017). Temporal and spatial coherence verification in SMIL documents with hoare logic and disjunctive constraints: A hybrid formal method. Journal of Integrated Design and Process Science, 20(3), 39-70. https://doi.org/10.3233/jid-2016-0020

© 2016 - Society for Design and Process Science. All rights reserved. The challenging problem of formal verification of SMIL (Synchronized Multimedia Integration Language Specification) documents is considered in this paper, where we propose a hybrid... Read More about Temporal and spatial coherence verification in SMIL documents with hoare logic and disjunctive constraints: A hybrid formal method.

Energy-Aware Constrained Relay Node Deployment for Sustainable Wireless Sensor Networks (2017)
Journal Article
Djenouri, D., & Bagaa, M. (2017). Energy-Aware Constrained Relay Node Deployment for Sustainable Wireless Sensor Networks. IEEE Transactions on Sustainable Computing, 2(1), 30-42. https://doi.org/10.1109/TSUSC.2017.2666844

© 2016 IEEE. This paper considers the problem of communication coverage for sustainable data forwarding in wireless sensor networks, where an energy-aware deployment model of relay nodes (RNs) is proposed. The model used in this paper considers const... Read More about Energy-Aware Constrained Relay Node Deployment for Sustainable Wireless Sensor Networks.

Energy Harvesting Aware Minimum Spanning Tree for Survivable WSN with Minimum Relay Node Addition (2017)
Conference Proceeding
Djenouri, D., Bagaa, M., Chelli, A., & Balasingham, I. (2017). Energy Harvesting Aware Minimum Spanning Tree for Survivable WSN with Minimum Relay Node Addition. https://doi.org/10.1109/glocomw.2016.7848891

Survivable wireless sensor networks that take advantage of green energy resources from the environment is considered in this paper. The particular problem of constrained relay nodes (RNs) placement to ensure communication coverage in the single-tiere... Read More about Energy Harvesting Aware Minimum Spanning Tree for Survivable WSN with Minimum Relay Node Addition.